A three-step sub-seasonal climate prediction method and system for multi-model ensemble sea temperature

Abstract

The present invention relates to the field of climate forecasting technology, and in particular to a multi-model ensemble sea temperature three-step sub-seasonal climate forecasting method and system. The method comprises the following steps: obtaining sea surface temperature data and sea ice coverage data, and pre-processing the sea surface temperature data and the sea ice coverage data to obtain standard input data; constructing an input field for an atmospheric circulation model based on the standard input data; inputting the input field into the atmospheric circulation model, and obtaining atmospheric circulation information and surface information based on the atmospheric circulation model; and performing dynamic downscaling based on a regional climate model according to the atmospheric circulation information and the surface information to obtain high-precision sub-seasonal climate forecast results. The present invention effectively solves the problem of insufficient accuracy of existing forecasting technologies, improves the accuracy and stability of sub-seasonal climate forecasts, provides new ideas for sub-seasonal climate forecasts, improves sub-seasonal climate forecasting techniques, and enhances the accuracy of forecast products.

Description

Technical Field

[0001] The present invention relates to the technical field of climate forecasting, and in particular to a multi-model ensemble sea temperature three-step sub-seasonal climate forecasting method and system. Background Art

[0002] The World Meteorological Organization's World Weather Research Programme and World Climate Research Programme jointly launched the Subseasonal-Seasonal Prediction Program, which aims to support subseasonal forecasting needs in agriculture, energy, transportation, and other sectors. Therefore, strengthening the research and application of refined subseasonal climate forecasting technologies has important scientific significance and broad societal application value.

[0003] Currently, Europe and the United States are able to provide subseasonal to seasonal climate forecast products. These products perform well in low-latitude regions, but their performance in mid- and high-latitude regions, particularly in East Asia, is relatively poor. With the introduction of the China Climate Services Framework, meteorological services for major events urgently require high-resolution subseasonal climate forecast products. However, current mainstream international global climate models cannot meet the required precision and accuracy. Therefore, research into refined subseasonal forecasting technologies is urgent.

[0004] Sea surface temperature anomalies are a key external forcing factor affecting the subseasonal variability of the East Asian summer monsoon and flood season precipitation. However, insufficient understanding of how external forcing factors such as sea surface temperature influence the atmospheric circulation and subseasonal precipitation has significantly hindered the understanding of the subseasonal predictability of the East Asian summer monsoon and the improvement of subseasonal forecast capabilities.

[0005] In view of this, the present invention discloses a multi-model ensemble sea temperature three-step sub-seasonal climate prediction method and system. First, the sea surface temperature data and sea ice coverage data of multiple sets of advanced global sea-land-air-ice coupled climate system models are obtained, and after statistical correction, pre-processing, and ensemble averaging, they are input into the global atmospheric circulation model to perform non-sea-air coupled prediction, and the atmospheric circulation information is extracted to drive the regional climate model to perform dynamic downscaling to obtain high-precision sub-seasonal climate prediction results. It effectively solves the problem of insufficient accuracy of existing prediction technology, improves the accuracy and stability of sub-seasonal forecasts, provides new ideas for sub-seasonal predictions, can improve the climate model's sub-seasonal prediction skills for China, and enhances the accuracy of prediction products. Summary of the Invention

[0006] In response to the deficiencies in the prior art, the present invention provides a multi-model ensemble sea temperature three-step sub-seasonal climate prediction method and system.

[0007] In order to achieve the above-mentioned objectives, in a first aspect, the present invention provides a multi-model ensemble sea temperature three-step sub-seasonal climate prediction method, the method comprising the following steps: acquiring sea surface temperature data and sea ice coverage data, and preprocessing the sea surface temperature data and the sea ice coverage data to obtain standard input data; constructing an input field of an atmospheric circulation model based on the standard input data; inputting the input field into the atmospheric circulation model, and obtaining atmospheric circulation information and surface information based on the atmospheric circulation model; and performing dynamic downscaling based on a regional climate model according to the atmospheric circulation information and the surface information to obtain high-precision sub-seasonal climate prediction results. The present invention significantly improves the spatiotemporal resolution and reliability of high-precision sub-seasonal climate forecast results through multi-source data fusion and multi-model collaborative optimization; standardized preprocessing ensures the spatiotemporal consistency of sea temperature and sea ice data, providing high-quality input for the model; the atmospheric circulation model couples surface information to accurately depict the key processes of ocean-air interaction; finally, the regional climate model dynamic downscaling technology effectively captures local climate characteristics, breaking through the resolution limitations of traditional global models; and achieves high-precision predictions of sub-seasonal climate events such as extreme weather and seasonal transitions.

[0008] Optionally, the acquisition of sea surface temperature data and sea ice coverage data includes: acquiring sea surface temperature prediction products and sea ice coverage prediction products from multiple sets of advanced global sea-land-air-ice coupled climate system models; obtaining the sea surface temperature data based on the sea surface temperature prediction products; and obtaining the sea ice coverage data based on the sea ice coverage prediction products. The present invention obtains sea temperature and sea ice prediction products through a multi-model set, fully utilizing the advantages of multi-source models, effectively reducing the uncertainty of a single model, and improving the spatiotemporal coverage and physical consistency of the data. It integrates key element data of sea-air coupling to accurately depict the thermal state of the ocean, provide more reliable data support for subsequent atmospheric circulation simulations, and provide a solid data foundation for sub-seasonal climate forecasts.

[0009] Optionally, the preprocessing of the sea surface temperature data and the sea ice coverage data to obtain standard input data includes: judging missing data or empty data of the sea surface temperature data and the sea ice coverage data to obtain a judgment result; based on the judgment result, cyclically filling the missing data or the empty data using the neighborhood interpolation method; after cyclic filling, improving the horizontal resolution of the sea surface temperature data and the sea ice coverage data using the linear interpolation method; combining the horizontal resolution, modifying the sea surface temperature data and the sea ice coverage data according to the grid resolution of the input field of the atmospheric circulation model to obtain the standard input data. The present invention significantly improves data quality and model adaptability through multi-level optimization; the cyclic filling strategy based on the neighborhood interpolation method effectively repairs data holes, ensures the continuity of the physical field through spatial correlation constraints, and avoids false mutations in model operation; after linear interpolation improves the resolution, the ability to capture ocean phenomena is enhanced; by dynamically matching the grid resolution of the atmospheric circulation model, the scale matching of the input field and the model architecture is achieved, ensuring the computational efficiency of sub-seasonal climate forecasting.

[0010] Optionally, the improving the horizontal resolution of the sea surface temperature data and the sea ice coverage data by using a linear interpolation method includes: in a one-dimensional case, the linear interpolation method satisfies the following relationship:

[0011] ;

[0012] in, is the value of the new grid point, The new grid and the original data grid The distance between The original data grid The corresponding value, The new grid and the original data grid The distance between The original data grid The present invention significantly optimizes data quality through spatial continuity constraints; the new grid points are calculated based on the weighted distance of adjacent grid points, and a smooth spatial transition is achieved through weight distribution, effectively eliminating the step-like mutations caused by insufficient resolution of the original data; the key features of the sea temperature gradient are retained, the resolution of the sea ice edge transition zone is improved, and a more refined sea-air interface thermal driving field is provided for the atmospheric circulation model, which significantly improves the physical authenticity of the initial field of the sub-seasonal forecast.

[0013] Optionally, constructing the input field of the atmospheric circulation model based on the standard input data includes: performing an equal-weighted averaging operation on the sea surface temperature data according to the standard input data to obtain ensemble mean sea surface temperature data; performing an equal-weighted averaging operation on the sea ice coverage data according to the standard input data to obtain ensemble mean sea ice coverage data; and using the ensemble mean sea surface temperature data and the ensemble mean sea ice coverage data as the input field of the atmospheric circulation model. The present invention significantly improves the physical consistency of the input field through multi-model ensemble averaging. First, performing an equal-weighted averaging operation on the sea temperature and sea ice data effectively eliminates the random errors and systematic biases of single-model predictions, making the ocean boundary conditions closer to the actual state. Second, the ensemble averaging process smoothes data noise, strengthens the spatial continuity of the ocean thermal signal, and provides a more stable driving field for the atmospheric circulation model. The ultimately constructed input field reduces the risk of overfitting and enhances predictability by retaining multi-model consensus information, providing a reliable physical basis for sub-seasonal climate prediction.

[0014] Optionally, performing an equal-weighted averaging operation on the sea surface temperature data to obtain ensemble mean sea surface temperature data includes: the ensemble mean sea surface temperature data satisfies the following relationship:

[0015] ;

[0016] in, is the ensemble mean sea surface temperature data, is the total number of patterns, is the index variable of the pattern, For the This paper significantly improves the reliability of the sea surface temperature field through multi-model fusion. The ensemble mean sea surface temperature data is obtained by equally weighted averaging the results of multiple models, effectively suppressing outliers caused by initial field errors or parameterization defects in a single model. It also preserves the independent climatological characteristics of each model, eliminates high-frequency noise through statistical averaging, and significantly enhances the credibility of sub-seasonal climate forecasts.

[0017] Optionally, performing an equal-weighted average operation on the sea ice coverage data to obtain ensemble average sea ice coverage data includes: the ensemble average sea ice coverage data satisfies the following relationship:

[0018] ;

[0019] in, is the ensemble mean sea ice cover data, is the total number of patterns, is the index variable of the pattern, For the The present invention significantly improves the physical consistency of the sea ice field through multi-model fusion. The equal-weighted averaging effectively eliminates the discrete deviation caused by differences in sea ice parameterization schemes in a single model. It not only retains the independent expression of the sea ice thermodynamic and dynamic processes in each model, but also suppresses false oscillations through statistical averaging, effectively enhancing the simulation capability of the ocean-atmosphere coupling process in sub-seasonal climate forecasts.

[0020] Optionally, the inputting of the input field into the atmospheric circulation model and obtaining atmospheric circulation information and surface information based on the atmospheric circulation model include: inputting the input field into the atmospheric circulation model and obtaining a non-sea-air coupling prediction result based on the atmospheric circulation model; extracting the atmospheric circulation information and the surface information based on the non-sea-air coupling prediction result. The present invention achieves efficient key variable extraction by operating in a non-sea-air coupling mode; first, the one-way drive mode greatly reduces computational complexity, quickly generates atmospheric circulation information and surface parameters, and significantly shortens prediction time; second, the modular design effectively isolates the ocean forcing signal from the atmospheric response process, facilitating the quantification of the independent impact of ocean boundary conditions on atmospheric circulation; finally, the extracted high-precision initial field provides temporally and spatially consistent data input for the regional climate model.

[0021] Optionally, the method of obtaining high-precision sub-seasonal climate prediction results by performing dynamic downscaling based on a regional climate model according to the atmospheric circulation information and the surface information includes: obtaining the initial field and lateral boundary conditions of the regional climate model according to the atmospheric circulation information and the surface information; and driving the regional climate model to perform dynamic downscaling based on the initial field and the lateral boundary conditions to obtain the high-precision sub-seasonal climate prediction results. The present invention significantly improves the refinement level of sub-seasonal predictions through dynamic downscaling; constructs high-resolution initial fields and lateral boundary conditions using the atmospheric circulation field, effectively transmits large-scale circulation signals to the regional climate model, and ensures the physical consistency of the sea-air interaction process; and then achieves dynamic downscaling through the regional climate model, effectively improving the accuracy of high-precision sub-seasonal climate prediction results and providing more accurate climate guidance for industry decision-making.

[0022] In a second aspect, the present invention provides a multi-model ensemble sea temperature three-step sub-seasonal climate prediction system, which executes the multi-model ensemble sea temperature three-step sub-seasonal climate prediction method provided by the present invention. The system includes an input device, an output device, a processor, and a memory. Its benefits are: the hardware facilities integrated by the present invention have excellent performance, the input device, output device, processor, and memory are interconnected, and information transmission between each component is smooth. Through the interaction of multiple hardware facilities, an efficient information processing system is constructed. By integrating high-performance hardware facilities, the present invention significantly improves the efficiency of sub-seasonal climate prediction; optimizes the resource utilization of regional climate model dynamic downscaling, improves the overall effectiveness of the prediction system, and provides stable and reliable technical support for sub-seasonal climate prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of a three-step multi-model ensemble sea temperature sub-seasonal climate prediction method according to an embodiment of the present invention;

[0024] Figure 2 A comparison diagram of the climatological distribution of rain belt positions according to an embodiment of the present invention;

[0025] Figure 3 This is a framework diagram of a multi-model ensemble sea temperature three-step sub-seasonal climate prediction system according to an embodiment of the present invention;

[0026] Figure 4 This is an execution flow chart of the multi-model ensemble sea temperature three-step sub-seasonal climate prediction system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] Specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the present invention. In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that these specific details are not necessarily required to practice the present invention. In other instances, well-known circuits, software, or methods are not specifically described to avoid obscuring the present invention.

[0028] Throughout this specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Therefore, appearances of the phrases "in one embodiment," "in an embodiment," "an example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combinations and / or subcombinations in one or more embodiments or examples. Furthermore, those of ordinary skill in the art will appreciate that the figures provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0029] See Figure 1 An embodiment of the present invention provides a three-step sub-seasonal climate prediction method for sea temperature using a multi-model ensemble, the method comprising the following steps:

[0030] S1. Acquire sea surface temperature data and sea ice coverage data, and preprocess the sea surface temperature data and the sea ice coverage data to obtain standard input data.

[0031] In this embodiment, sea surface temperature prediction products and sea ice coverage prediction products of multiple advanced domestic and foreign global sea-land-air-ice coupled climate system models in the sub-seasonal prediction business are obtained as sub-seasonal model products.

[0032] Specifically, considering data quality and timeliness, relevant forecasting capability analysis literature was researched to identify the advanced global ocean-land-air-ice coupled climate system model products needed, and subseasonal model products were obtained. The global ocean-land-air-ice coupled climate system models selected included the Beijing Climate Centre Sub-seasonal to Seasonal and Interannual Prediction System Version 3 (CPSv3) model of the China Meteorological Administration, the Integrated Forecasting System (IFS) model of the European Centre for Medium-Range Weather Forecasts, and the Climate Forecast System Version 2 (CFSv2) model of the U.S. National Centers for Environmental Prediction.

[0033] Furthermore, based on the CPSv3 model, IFS model and CFSv2 model as a multi-model set, the sea surface temperature prediction products and sea ice coverage prediction products of the CPSv3 model, IFS model and CFSv2 model are obtained respectively, and the sea surface temperature data are obtained based on the sea surface temperature prediction products, and the sea ice coverage data are obtained based on the sea ice coverage prediction products.

[0034] In this embodiment, the sea surface temperature data and sea ice coverage data under the above-mentioned multi-model set are preprocessed so that they can be used as input fields for the atmospheric circulation model, including: determining whether the sea surface temperature data and sea ice coverage data contain missing data or empty data for land areas; if the land areas contain missing data or empty data, using a neighborhood interpolation method to cyclically fill in the missing data or empty data for the land areas based on the sea surface temperature data or sea ice coverage data at the sea-land junction; using linear interpolation to increase the horizontal resolution of the filled sea surface temperature data and sea ice coverage data to be higher than the spatial resolution of the input field of the atmospheric circulation model; and modifying the high-precision sea surface temperature data and sea ice coverage data according to the grid scale of the input field of the atmospheric circulation model to obtain standard input data.

[0035] Specifically, the preprocessing of sea surface temperature data and sea ice coverage data includes the following steps:

[0036] First, determine whether the sea surface temperature data and sea ice cover data contain missing or empty data for land areas.

[0037] Secondly, if there are missing or empty data for land areas, the missing or empty data for land areas are filled in cyclically using the neighborhood interpolation method based on the sea surface temperature data or sea ice coverage data of the land-sea junction zone, as follows:

[0038] The first step is to count the missing data and record the coordinates. The missing data grid points in the sea surface temperature data or sea ice coverage data are counted to get the number of missing data grid points. The number of missing data grid points is expressed as miss. num At the same time, create two arrays, each of which is represented by x grid and y grid To represent it, the two arrays are used to accurately record the coordinate position of each missing data grid point in the data matrix.

[0039] The second step is to determine the neighborhood of the missing points and adjust the boundaries. num>0), the following operation is performed for each missing point: the range of one grid point around the missing point is determined, that is, the neighborhood formed by one grid point above, below, left, and right of the missing point. If the coordinates of some points in the neighborhood exceed the range of the data itself, for example, exceeding the number of rows or columns of the data matrix, these out-of-range coordinates are adjusted accordingly to ensure that the neighborhood range is within the data range.

[0040] The third step is to calculate the neighborhood average and fill in the missing points. After determining the valid neighborhood range of the missing point, extract the sea surface temperature and sea ice cover data within that neighborhood. The sea surface temperature and sea ice cover data are processed, their average values ​​are calculated, and the resulting average values ​​are assigned to the corresponding missing points, completing the filling operation.

[0041] The fourth step is to count the missing data again and update the coordinate records. After completing a round of filling operations, re-count the number of missing data grid points in the data. If there is still missing data at this time, that is, the number of missing data grid points is not zero, re-create x grid and y grid Array. The grid coordinates that are still missing data are stored in the two newly created arrays in preparation for the next round of filling operations.

[0042] Step 5: Loop the filling operation until there are no missing data points. Continue looping from step 1 to step 4. Each round of loop processes the missing data until there are no missing data in the data, that is, the number of missing data points becomes zero (missing data grid points). num =0), then the loop stops and the filling of missing data in the land area is completed.

[0043] Then, linear interpolation is used to increase the horizontal resolution of the filled sea surface temperature data and sea ice cover data to make it higher than the spatial resolution of the atmospheric circulation model input field, as follows:

[0044] The first step is to clarify the input and output requirements. This involves determining the infilled sea surface temperature and sea ice cover data whose resolution needs to be improved, and the desired spatial resolution above that of the GCM input field. Furthermore, the relevant parameters for linear interpolation, such as the interpolation direction and accuracy requirements, must be clearly defined.

[0045] The second step is to set the target resolution. Based on the spatial resolution of the ACM input field, set a higher target resolution. This target resolution will determine the grid density of the newly generated data. For example, if the original data resolution is one grid point per 1.5 degrees, and the ACM input field spatial resolution is one grid point per 1 degree, the target resolution can be set to one grid point per 0.5 degrees.

[0046] The third step is to perform linear interpolation. For each grid point in the new data frame, a linear interpolation algorithm is used to calculate the value of that grid point using the values ​​of adjacent grid points in the original data. The linear interpolation algorithm takes a weighted average of the values ​​of adjacent grid points in the original data, based on the relative position of the new grid point among the grid points in the original data.

[0047] In the one-dimensional case, if the new grid point is located between the original data grid points x1 and x2, the distance between the new grid point and the original data grid point x1 is d1, and the distance between the new grid point and the original data grid point x2 is d2, then the value of the new grid point satisfies the following calculation formula:

[0048]

[0049] in, is the value of the new grid point, The new grid and the original data grid The distance between The original data grid The corresponding value, The new grid and the original data grid The distance between The original data grid The corresponding value.

[0050] In two-dimensional data, bilinear interpolation is required to comprehensively consider the values ​​of adjacent grid points in two directions.

[0051] Step 4: Check and adjust. After calculating all new grid point values, check the generated high-resolution data. Check for outliers, such as values ​​outside a reasonable range or values ​​that do not conform to the data trend. If outliers exist, analyze the cause. This may be due to improper interpolation algorithm parameter settings or problems with the original data. Based on the analysis results, make appropriate adjustments, such as resetting interpolation parameters or preprocessing the original data.

[0052] Among them, the reasonable range is set as: sea surface temperature data appears below -10 or greater than 100, and sea ice coverage data appears less than 0 or greater than 1.

[0053] Finally, according to the grid resolution of the atmospheric circulation model input field, the high-resolution sea surface temperature data and sea ice coverage data are processed into the grid resolution of the atmospheric circulation model input field using the linear interpolation method to obtain the standard input data.

[0054] S2. Constructing an input field of an atmospheric circulation model based on the standard input data.

[0055] In this embodiment, the pre-processed multi-mode sea surface temperature data and sea ice coverage data are subjected to equal-weighted averaging to obtain ensemble mean sea surface temperature data and ensemble mean sea ice coverage data.

[0056] Specifically, the sea surface temperature data are subjected to equal weighted averaging to obtain the ensemble mean sea surface temperature data, which satisfies the following relationship:

[0057]

[0058] in, is the ensemble mean sea surface temperature data, is the total number of patterns, is the index variable of the pattern, For the Sea surface temperature data for each model.

[0059] Specifically, the sea ice coverage data are averaged by performing equal weighted averaging on the sea ice coverage data to obtain the ensemble average sea ice coverage data, which satisfies the following relationship:

[0060]

[0061] in, is the ensemble mean sea ice cover data, is the total number of patterns, is the index variable of the pattern, For the Sea ice cover data for each model.

[0062] Furthermore, the obtained ensemble mean sea surface temperature data and ensemble mean sea ice cover data are used as input fields for the atmospheric circulation model.

[0063] S3. Inputting the input field into the atmospheric circulation model, and obtaining atmospheric circulation information and surface information based on the atmospheric circulation model.

[0064] In this embodiment, the input field is input into the atmospheric circulation model, and a non-sea-air coupling prediction result is obtained based on the atmospheric circulation model; atmospheric circulation information and surface information are extracted based on the non-sea-air coupling prediction result.

[0065] Specifically, the above-mentioned revised ensemble mean sea surface temperature data and ensemble mean sea ice cover data are input into the atmospheric circulation model, and the atmospheric circulation model is freely run to obtain the non-sea-air coupling prediction results for the next 0-60 days. The required atmospheric circulation information and surface information are extracted from the non-sea-air coupling prediction results, and the atmospheric circulation information and surface information are used to drive the regional climate model.

[0066] It should be noted that since the atmospheric circulation model is used, the operation of the atmospheric circulation model is a non-sea-air coupling simulation.

[0067] The atmospheric circulation information and surface information include but are not limited to the 6-hour average surface pressure, sea surface pressure, surface air temperature, 10-meter meridional wind, 10-meter zonal wind, whole-layer absolute humidity, whole-layer air temperature, whole-layer meridional wind, whole-layer zonal wind, geopotential height, surface temperature, soil moisture, snow cover and sea surface temperature.

[0068] S4. Based on the atmospheric circulation information and the surface information, dynamic downscaling is performed based on a regional climate model to obtain a high-precision sub-seasonal climate forecast result.

[0069] In this embodiment, the initial field and lateral boundary conditions of the regional climate model are obtained based on atmospheric circulation information and surface information; based on the initial field and lateral boundary conditions, the regional climate model is driven to achieve dynamic downscaling to obtain high-precision sub-seasonal climate forecast results.

[0070] Specifically, the atmospheric circulation information and surface information generated by the atmospheric circulation model are converted into the initial field and lateral boundary conditions of the regional climate model using linear interpolation.

[0071] Furthermore, the above initial fields and lateral boundary conditions are used to drive the regional climate model to achieve dynamic downscaling, and the regional climate model is run to generate high-precision prediction results for the next 0-60 days as high-precision sub-seasonal climate prediction results.

[0072] S5. Verify and evaluate the high-precision sub-seasonal climate forecast results.

[0073] In an optional embodiment, the present invention constructs a multi-model ensemble sea temperature three-step sub-seasonal climate prediction method (represented by Tier-3) which is significantly better than the traditional two-step sub-seasonal climate prediction method (represented by Tier-2) in sea surface temperature prediction performance.

[0074] See Figure 2 The figure shows a comparison of the climatological distribution of rain belt locations, comparing the observed and predicted climatological distribution of the rain belt locations from June to August, averaged from 2008 to 2023. Left: Observed climatological distribution of rain belt locations; Top right: Schematic diagram of simulated results for Tier-2 rain belt locations; Bottom right: Schematic diagram of simulated results for Tier-3 rain belt locations.

[0075] based on Figure 2It can be seen that the Tier-3 method shows significantly better performance than the Tier-2 method in sub-seasonal precipitation forecasting, especially in the simulation of regional precipitation and the dynamic evolution of rain belt position. The Tier-2 method has a significant overestimation phenomenon: the simulated regional average precipitation intensity reaches 8mm / d, which is about 70% higher than the observed value (4.5mm / d). The Tier-3 method effectively corrects this deviation by optimizing the coupling mechanism between the initial sea temperature field and the atmospheric model: the error of the predicted regional average precipitation intensity is reduced to about 25%. Based on the rain belt position distribution map of the 2008-2023 climate state average ( Figure 2 ) shows that the traditional Tier-2 method predicted a weak northward shift of the rain belt, failing to accurately predict the location of the rain belt in southern Northeast China in mid-to-late August. In contrast, the Tier-3 method successfully captured the trend of the rain belt advancing from the Yangtze River Basin to North China and Northeast China 2-3 pentads in advance, demonstrating a significant advantage in predicting the northward shift of the rain belt from late July to August (10-18 pentads). The improved predictive capability of the Tier-3 method provides scientific support for responding to extreme weather during the flood season in northern China.

[0076] The multi-model ensemble three-step sub-seasonal sea temperature prediction method constructed by the present invention makes up for the incoordination problem of atmospheric, oceanic and land surface information in the current two-step sub-seasonal climate prediction, effectively improves the climate model's sub-seasonal prediction skills for China, and can enhance the accuracy of current sub-seasonal climate prediction products.

[0077] See Figure 3 In an optional embodiment, the present invention provides a multi-model ensemble sea temperature three-step sub-seasonal climate prediction system. The system includes an input device, an output device, a processor, and a memory, wherein the hardware facilities are interconnected. The memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the specific steps of the embodiments of the multi-model ensemble sea temperature three-step sub-seasonal climate prediction method provided by the present invention. The multi-model ensemble sea temperature three-step sub-seasonal climate prediction system provided by the present invention has a complete structure, objective stability, and enhances the overall applicability and practical application capabilities of the present invention.

[0078] See Figure 4 The figure shows the execution flow chart of the multi-model ensemble sea temperature three-step sub-seasonal climate prediction system; it shows in detail the operation process of the sub-seasonal climate prediction system and describes the specific sub-seasonal climate prediction processing process of the system processor.

[0079] In an optional embodiment, the multi-model ensemble sea temperature three-step sub-seasonal climate prediction system includes a data acquisition module, a data processing module, an ensemble average module, a global sub-seasonal prediction module and a regional dynamic downscaling module.

[0080] Specifically, the data acquisition module is used to obtain sea surface temperature data and sea ice coverage data from the advanced global sea-land-air-ice coupled climate system model; the data processing module is used to statistically correct the sea surface temperature data and sea ice coverage data; the ensemble averaging module is used to ensemble average the statistically corrected sea surface temperature data and sea ice coverage data and input them into the global atmospheric circulation model; the global sub-seasonal prediction module is used to generate atmospheric circulation information and surface information that drives the regional model; and the regional dynamic downscaling module is used to obtain high-precision sub-seasonal forecast results for the prediction area.

[0081] In summary, the method of the present invention provides a multi-model ensemble sea temperature three-step sub-seasonal climate prediction method and system, which obtains sea surface temperature data and sea ice coverage data from an advanced global sea-land-air-ice coupled climate system model; corrects and ensemble averages the sea surface temperature data and sea ice coverage data and inputs them into a global atmospheric circulation model to perform a non-sea-air coupled simulation run; then, the non-sea-air coupled prediction results of the atmospheric circulation model are input as initial boundary conditions into a regional model for dynamic downscaling, thereby obtaining high-precision regional climate prediction results for the next 0-60 days; improves current sub-seasonal forecasting skills, provides new ideas for sub-seasonal forecasting, and effectively improves the precision and accuracy of sub-seasonal forecasting. The method of the present invention is easy to understand, simple to calculate, has a small workload, is convenient for engineering application, and provides a theoretical basis and technical support for the further development of climate forecasting technology.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A three-step multi-model ensemble sea temperature sub-seasonal climate prediction method, characterized by: The steps include: Acquiring sea surface temperature data and sea ice coverage data, and preprocessing the sea surface temperature data and the sea ice coverage data to obtain standard input data; constructing an input field of an atmospheric circulation model based on the standard input data; Inputting the input field into the atmospheric circulation model, and obtaining atmospheric circulation information and surface information based on the atmospheric circulation model; According to the atmospheric circulation information and the surface information, dynamic downscaling is performed based on a regional climate model to obtain a high-precision sub-seasonal climate forecast result; The preprocessing of the sea surface temperature data and the sea ice coverage data to obtain standard input data includes: Determining missing data or empty data of the sea surface temperature data and the sea ice coverage data to obtain a determination result; Based on the judgment result, the missing data or the empty data is cyclically filled using a neighborhood difference method; After the cyclic filling, the horizontal resolution of the sea surface temperature data and the sea ice coverage data is improved by using a linear interpolation method; In combination with the horizontal resolution, the sea surface temperature data and the sea ice coverage data are modified according to the grid resolution of the input field of the atmospheric circulation model to obtain the standard input data; The method of improving the horizontal resolution of the sea surface temperature data and the sea ice coverage data by using a linear interpolation method includes: In the one-dimensional case, the linear interpolation method satisfies the following relationship: in, is the value of the new grid point, The new grid and the original data grid The distance between The original data grid The corresponding value, The new grid and the original data grid The distance between The original data grid The corresponding value.

2. The multi-model ensemble sea temperature three-step sub-seasonal climate prediction method according to claim 1 is characterized in that: The obtaining of sea surface temperature data and sea ice coverage data includes: Obtain sea surface temperature prediction products and sea ice cover prediction products from multiple sets of advanced global ocean-land-air-ice coupled climate system models; Obtaining the sea surface temperature data based on the sea surface temperature prediction product; The sea ice coverage data is obtained according to the sea ice coverage prediction product.

3. The multi-model ensemble sea temperature three-step sub-seasonal climate prediction method according to claim 1 is characterized in that: The step of constructing an input field of an atmospheric circulation model based on the standard input data comprises: performing an equal-weighted average operation on the sea surface temperature data according to the standard input data to obtain ensemble mean sea surface temperature data; performing an equal-weighted average operation on the sea ice coverage data according to the standard input data to obtain ensemble average sea ice coverage data; The ensemble mean sea surface temperature data and the ensemble mean sea ice cover data are used as input fields of the atmospheric circulation model.

4. The multi-model ensemble sea temperature three-step sub-seasonal climate prediction method according to claim 3 is characterized in that: The performing an equal-weighted average operation on the sea surface temperature data to obtain ensemble mean sea surface temperature data includes: The ensemble mean sea surface temperature data satisfies the following relationship: in, is the ensemble mean sea surface temperature data, is the total number of patterns, is the index variable of the pattern, For the Sea surface temperature data for each model.

5. The multi-model ensemble sea temperature three-step sub-seasonal climate prediction method according to claim 3 is characterized in that: The performing an equal-weighted average operation on the sea ice coverage data to obtain ensemble average sea ice coverage data includes: The ensemble mean sea ice coverage data satisfies the following relationship: in, is the ensemble mean sea ice cover data, is the total number of patterns, is the index variable of the pattern, For the Sea ice cover data for each model.

6. The multi-model ensemble sea temperature three-step sub-seasonal climate prediction method according to claim 1, characterized in that: Inputting the input field into the atmospheric circulation model, and obtaining atmospheric circulation information and surface information based on the atmospheric circulation model, includes: Inputting the input field into the atmospheric circulation model, and obtaining a non-sea-air coupled prediction result based on the atmospheric circulation model; The atmospheric circulation information and the surface information are extracted based on the non-sea-air coupling prediction results.

7. The multi-model ensemble sea temperature three-step sub-seasonal climate prediction method according to claim 1, characterized in that: The method of obtaining a high-precision sub-seasonal climate forecast result by performing dynamic downscaling based on a regional climate model according to the atmospheric circulation information and the surface information includes: Obtaining the initial field and lateral boundary conditions of the regional climate model based on the atmospheric circulation information and the surface information; The regional climate model is driven to realize dynamic downscaling based on the initial field and the lateral boundary conditions to obtain the high-precision sub-seasonal climate prediction result.

8. A three-step multi-model ensemble sea temperature sub-seasonal climate prediction system, characterized by: The system includes an input device, an output device, a processor and a memory, wherein the input device, the output device, the processor and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the multi-model ensemble sea temperature three-step sub-seasonal climate prediction method according to any one of claims 1 to 7.

Images